# Implicit Neural Multiple Description for DNA-based data storage

arXiv: https://arxiv.org/abs/2309.06956 · HTML: https://mlsanity.com/paper/2309.06956/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [Implicit Neural Representations for Image Compression](https://mlsanity.com/paper/2112.04267/) (2021)
- [End-to-end optimization of nonlinear transform codes for perceptual quality](https://mlsanity.com/paper/1607.05006/) (2016)
- [COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codec](https://mlsanity.com/paper/2212.05458/) (2022)
- [Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective](https://mlsanity.com/paper/2201.01741/) (2022)
- [Multiple Description Convolutional Neural Networks for Image Compression](https://mlsanity.com/paper/1801.06611/) (2018)
- [A biologically constrained encoding solution for long-term storage of images onto synthetic DNA](https://mlsanity.com/paper/1904.03024/) (2019)
- [A constrained Shannon-Fano entropy coder for image storage in synthetic DNA](https://mlsanity.com/paper/2203.09988/) (2022)
- [MQ-Coder inspired arithmetic coder for synthetic DNA data storage](https://mlsanity.com/paper/2306.12708/) (2023)
- [INR-MDSQC: Implicit Neural Representation Multiple Description Scalar Quantization for robust image Coding](https://mlsanity.com/paper/2306.13919/) (2023)

## Abstract (arXiv)

DNA exhibits remarkable potential as a data storage solution due to its impressive storage density and long-term stability, stemming from its inherent biomolecular structure. However, developing this novel medium comes with its own set of challenges, particularly in addressing errors arising from storage and biological manipulations. These challenges are further conditioned by the structural constraints of DNA sequences and cost considerations. In response to these limitations, we have pioneered a novel compression scheme and a cutting-edge Multiple Description Coding (MDC) technique utilizing neural networks for DNA data storage. Our MDC method introduces an innovative approach to encoding data into DNA, specifically designed to withstand errors effectively. Notably, our new compression scheme overperforms classic image compression methods for DNA-data storage. Furthermore, our approach exhibits superiority over conventional MDC methods reliant on auto-encoders. Its distinctive strengths lie in its ability to bypass the need for extensive model training and its enhanced adaptability for fine-tuning redundancy levels. Experimental results demonstrate that our solution competes favorably with the latest DNA data storage methods in the field, offering superior compression rates and robust noise resilience.